By default Elasticsearch indexes and adds doc values to most fields so that they
can be searched and aggregated out of the box. For instance if you have a numeric
field called foo that you need to run histograms on but that you never need to
filter on, you can safely disable indexing on this field in your
mappings:

text fields store normalization factors in the index in order to be
able to score documents. If you only need matching capabilities on a text
field but do not care about the produced scores, you can configure Elasticsearch
to not write norms to the index:

text fields also store frequencies and positions in the index by
default. Frequencies are used to compute scores and positions are used to run
phrase queries. If you do not need to run phrase queries, you can tell
Elasticsearch to not index positions:

Furthermore if you do not care about scoring either, you can configure
Elasticsearch to just index matching documents for every term. You will
still be able to search on this field, but phrase queries will raise errors
and scoring will assume that terms appear only once in every document.

The default dynamic string mappings will index string fields
both as text and keyword. This is wasteful if you only
need one of them. Typically an id field will only need to be indexed as a
keyword while a body field will only need to be indexed as a text field.

This can be disabled by either configuring explicit mappings on string fields
or setting up dynamic templates that will map string fields as either text
or keyword.

For instance, here is a template that can be used in order to only map string
fields as keyword:

Larger shards are going to be more efficient at storing data. To increase the size of your shards, you can decrease the number of primary shards in an index by creating indices with less primary shards, creating less indices (e.g. by leveraging the Rollover API), or modifying an existing index using the Shrink API.

Keep in mind that large shard sizes come with drawbacks, such as long full recovery times.

Indices in Elasticsearch are stored in one or more shards. Each shard is a Lucene index and made up of one or more segments - the actual files on disk. Larger segments are more efficient for storing data.

The _forcemerge API can be used to reduce the number of segments per shard. In many cases, the number of segments can be reduced to one per shard by setting max_num_segments=1.

The type that you pick for numeric data can have a significant impact
on disk usage. In particular, integers should be stored using an integer type
(byte, short, integer or long) and floating points should either be
stored in a scaled_float if appropriate or in the smallest type that fits the
use-case: using float over double, or half_float over float will help
save storage.

When Elasticsearch stores _source, it compresses multiple documents at once
in order to improve the overall compression ratio. For instance it is very
common that documents share the same field names, and quite common that they
share some field values, especially on fields that have a low cardinality or
a zipfian distribution.

By default documents are compressed together in the order that they are added
to the index. If you enabled index sorting
then instead they are compressed in sorted order. Sorting documents with similar
structure, fields, and values together should improve the compression ratio.